This work proposes a kinase-specific phosphorylation site prediction server which incorporates support vector machines (SVM) with two features, i.e. protein sequence profiles surrounding the modified sites and coupling patterns surrounding the modified sites. The coupling pattern of proteins, which is first used for analyzing the protein thermostability (14). In this work, we incorporate the protein coupling pattern as a feature for training computer models for identifying phosphorylation sites. After evaluating the computational models by k-fold cross-validation and Jackknife cross-validation, the overall predictive accuracy of KinasePhos 2.0 is ∼91%, which is better than the previous version and the other tools previously developed. The details of the proposed method and predictive performance are described below.
